Recently, sentence embeddings play an important role in information retrieval, especially in LLM-augmented retrieval. AnglE (https://github.com/SeanLee97/AnglE) is an open-source sentence embedding model. It shows good performance in Semantic Textual Similarity (STS) tasks and currently supports generating LLaMA-7B embeddings, which are state of the art in STS, as well as BERT-based embeddings. It also supports two languages: English and Chinese. LLaMA's powerful multilingual capability makes it easy to fine-tune the model for other languages.
